By acquiring Torch, a startup that unifies medical records for AI, OpenAI is moving beyond a general-purpose platform. This purchase provides crucial domain expertise and a solution for structured data, revealing a strategy to build specialized, industry-specific AI products for high-value sectors like healthcare.

To maintain clinical reliability, Open Evidence restricts its knowledge base to a 'walled garden.' This includes PubMed abstracts (excluding predatory journals), guidelines, and full-text content from licensed publishers. This prevents the model from citing unreliable internet sources or 'hallucinating' references, building clinician trust.

By analyzing millions of queries from clinicians, Open Evidence identifies high-frequency topics. It then cross-references these with its literature database to pinpoint areas that are both clinically relevant and poorly supported by existing evidence, effectively mapping the frontier of medical knowledge and research opportunities.

Open Evidence employs a two-step process that distinguishes it from general LLMs. First, its AI, trained by human subspecialists, identifies the most relevant scientific references for a query. Only then is an answer generated from this curated evidence, prioritizing source credibility over speed.

M&A Science's "intelligence hub" differentiates from generalist AI like ChatGPT by grounding answers in a closed ecosystem of 400+ expert interviews. It provides sourced, experiential intelligence rather than generic internet-scraped guesses, making it a reliable tool for high-stakes professional work.

An effective AI strategy in healthcare is not limited to consumer-facing assistants. A critical focus is building tools to augment the clinicians themselves. An AI 'assistant' for doctors to surface information and guide decisions scales expertise and improves care quality from the inside out.

The AI arms race will shift from building ever-larger general models to creating smaller, highly specialized models for domains like medicine and law. General AIs will evolve to act as "general contractors," routing user queries to the appropriate specialist model for deeper expertise.

Generic AI documentation tools, often trained on primary care conversations in quiet rooms, fail in specialized fields. Physical therapy occurs in noisy, dynamic environments with unique terminology. TheraNow's success came from building its AI on a specific dataset of PT-patient interactions, tailored to that workflow.

NVIDIA is creating customized versions of its general-purpose AI models, like Cosmos and Groot, for specific industries. By fine-tuning them on specialized data, such as surgical videos, they can power high-value, niche applications like surgical robots, demonstrating a vertical-focused go-to-market strategy.